13k_thesis_noise00.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Measure classification accuracy without noise. Final result for the thesis report and presentation.\n",
    "\n",
    "Conclusion: Structure increases accuracy by 2% in the absence of noise.\n",
    "\n",
    "Observations:\n",
    "* Extracted features increase accuracy by ~7% over baseline for all scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'lg'\n",
    "Pvalues = [None, 100]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\n",
    "regen_features = True\n",
    "regen_baseline = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = {}\n",
    "\n",
    "# Preprocessing.\n",
    "\n",
    "# Graph.\n",
    "p['data_scaling_graph'] = 'features'\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'euclidean'\n",
    "p['Csigma'] = 1\n",
    "p['diag'] = True\n",
    "p['laplacian'] = 'normalized'\n",
    "\n",
    "# Feature extraction.\n",
    "p['m'] = 128  # 64, 128, 512\n",
    "p['ls'] = 1\n",
    "p['ld'] = 10\n",
    "p['le'] = None\n",
    "p['lg'] = 100\n",
    "\n",
    "# Classification.\n",
    "p['scale'] = None\n",
    "p['Nvectors'] = 6\n",
    "p['svm_type'] = 'C'\n",
    "p['kernel'] = 'linear'\n",
    "p['C'] = 1\n",
    "p['nu'] = 0.5\n",
    "p['majority_voting'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['test_size'] = 0.1\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms_D']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 181.93 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.49506187439]\n",
      "w in [0.00518967304379, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2399924,), float32\n",
      "  L_indices : (2399924,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2399924,), float32\n",
      "  W_indices : (2399924,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 191.32 seconds\n",
      "\n",
      " -------------------- Features, lg = None -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2399924,), float32\n",
      "  L_indices : (2399924,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2399924,), float32\n",
      "  W_indices : (2399924,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 1148 seconds\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc24c0aa550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 898 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.231717e+04\n",
      "rdiff: 0.00242506942781\n",
      "i(Z) = ||Z||_1 = 6.887042e+04\n"
     ]
    },
    {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238be01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.111876e+05\n"
     ]
    },
    {
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       "XzgfeQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238a5e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 14 iterations\n",
      "\n",
      "Z in [-1.15960109234, 1.81123566628]\n",
      "Sparsity of Z: 905,112 non-zero entries out of 19,072,000 entries, i.e. 4.7%.\n"
     ]
    },
    {
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       "ERERERERERERERERERERERERERERERERESnL/wfWSjzRO45PqgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc29bcd4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.760848701, 0.938695788383]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238b80d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238d1da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238ae8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 1155 seconds\n",
      "\n",
      " -------------------- Classification, lg = None -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc22020e0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  76 (+/- 1.7) <- [75 75 77 75 71 76 76 76 76 77]\n",
      "  76 (+/- 1.3) <- [76 76 75 72 77 76 75 76 73 76]\n",
      "  77 (+/- 1.7) <- [77 75 78 75 76 78 79 75 78 73]\n",
      "  75 (+/- 1.2) <- [77 73 74 75 75 75 75 74 75 75]\n",
      "  75 (+/- 1.7) <- [76 75 77 75 74 77 76 72 73 72]\n",
      "  77 (+/- 1.3) <- [76 77 77 78 78 75 76 77 76 74]\n",
      "  75 (+/- 0.8) <- [75 75 76 76 74 74 76 75 76 74]\n",
      "  76 (+/- 1.5) <- [74 75 78 76 74 78 75 74 74 77]\n",
      "  75 (+/- 1.9) <- [76 76 72 75 74 72 74 78 77 75]\n",
      "  76 (+/- 2.0) <- [75 77 73 72 75 76 74 75 76 80]\n",
      "  76 (+/- 1.0) <- [75 76 74 76 77 75 75 74 75 77]\n",
      "  76 (+/- 1.4) <- [78 77 75 75 76 77 78 74 76 74]\n",
      "  76 (+/- 1.1) <- [77 77 75 75 76 76 75 73 75 75]\n",
      "  76 (+/- 2.0) <- [78 78 77 72 74 76 73 73 76 75]\n",
      "  77 (+/- 1.3) <- [78 78 75 78 77 76 78 78 76 75]\n",
      "  76 (+/- 2.2) <- [78 75 75 78 73 74 78 75 76 72]\n",
      "  77 (+/- 1.2) <- [76 76 76 75 76 75 77 79 78 77]\n",
      "  76 (+/- 1.7) <- [73 77 74 76 73 78 74 76 77 73]\n",
      "  76 (+/- 1.2) <- [76 76 74 73 75 77 77 75 74 75]\n",
      "  76 (+/- 1.2) <- [75 76 75 75 76 76 77 76 74 73]\n",
      "Accuracy: 75.9 (+/- 1.62)\n",
      "Mean time (20 cv): 32.65 seconds\n",
      "Overall time: 658.06 seconds\n",
      "\n",
      " -------------------- Features, lg = 100 -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2399924,), float32\n",
      "  L_indices : (2399924,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2399924,), float32\n",
      "  W_indices : (2399924,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 2781 seconds\n"
     ]
    },
    {
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       "+L5JwDvi4/dgf7cS9t1HsWlCVwZmx9eszdiWGpGuUo1cxOQ1qOux2u084FRs+sIRrOm6hDWBvzW+\n",
       "vljruhD4N+Bm7KSrKwvPfQ9rhr0G61PO3/dzYMf4mRl2EtdDWJ9wdY2umRpe/po/AnfHLLfEz623\n",
       "nazG47zA+xl20tgPse8PcDjW7D4JO0D5GHDfOPn+CGxH7ab36s+vlaf4+CJs/+T7dwl2Al71e+t5\n",
       "ECt4L8H+nudTOSei3vqnsYOhw4FF2MHXFGy/rBxf/x2sS4T42lavfBAREUnWbOwkwX7VyuWDk7CD\n",
       "QFWSpGfUtC4i3fYUVtPd1TvIBLXS370XcA52noKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\n",
       "l/8PqIDJDwbI1K8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc21bc77bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1329 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.124096e+04\n",
      "rdiff: 1.8094401784e-05\n",
      "i(Z) = ||Z||_1 = 5.681248e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.627869e+03\n"
     ]
    },
    {
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       "iIiIiIiIiIiIiIhIv/v/z2CeP0L288MAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2183cc490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.366813e+05\n"
     ]
    },
    {
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       "C5QfxWwX4L//bcCn8Yb5pvjzpS32M9SvaTb4T+O3A/8h/rs/j09iBDgh5r0ubi/2eTHwdpqTD4+M\n",
       "+7sJ+ANw6Ai/X6vf9RT8/9KbgXPxyZbPjbB9uOMBsCWD/6xFRESkjbXwxj5XBzH8dR2+gBctIsmo\n",
       "x0BEcvMkPufijamDjFKrHhzwYYSd8FM7RURERERERERERERERERERERERERERERERERERERy9f8B\n",
       "Hc8IcHrFfZcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2599a70d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.170815914869, 0.981470227242]\n",
      "Sparsity of Z: 3,737,193 non-zero entries out of 19,072,000 entries, i.e. 19.6%.\n"
     ]
    },
    {
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       "ew0dr0SX3PYGdHH556LbKLwTwFcD+KeivOXa3xTnNuherXsLjlvTnxdlfh6dt+BKdK744V/jPbr/\n",
       "/g50HovfMdojhBBCyAxcjM5VTgjpoSVOCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE\n",
       "EEIIIevi/wNumCjrZhGFbwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc218394310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0261658579111, 0.891849219799]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc24c06db10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc21bbc9dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc29841e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 2790 seconds\n",
      "\n",
      " -------------------- Classification, lg = 100 -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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       "zHXtyPv+iQWknw5S9kTMFvPtXyFtPsa6XYi7c52WkZ4ZYrl7r1Ccm3GJGH+fxr2kuhPw5rx/GX5I\n",
       "eizv7xNEu1dkocL5+A7Vv7LueTCf94imkl4jPMLzxRhfJfr1T2IM/0R4X1/AHqr6uN0niObbQkfc\n",
       "ilvyPTwrIlPbhc75o+jPK4QDsFs4E5uETs4yXe0g/TzqkXbWrvXqfH83i4jDo6T5IlGmQfIz8WXc\n",
       "JhKIXosfYIPwng9SbctEeTjXbbzgjfuxg6ou0/fgA1gpxvoGwRWrRdj2a4LwCblZnDnrTCGvzwgP\n",
       "cp0whCbktpkrHLzfar4Lky6JP92kcY1o3IKCtXjLEF1rjSJ3BYGhkrs1iswVNDGU+q6goKCgoKCg\n",
       "oKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoGAY8T8ZR8LbSBHBiQAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc25641d690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 63.7 %\n",
      "Clips accuracy: 73.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  77 (+/- 2.5) <- [77 74 77 77 70 78 76 79 79 77]\n",
      "  78 (+/- 1.6) <- [76 78 78 74 80 76 80 76 77 79]\n",
      "  79 (+/- 1.3) <- [79 79 78 76 79 81 77 77 78 77]\n",
      "  78 (+/- 1.3) <- [78 74 77 78 77 79 77 77 79 78]\n",
      "  78 (+/- 0.8) <- [79 77 77 77 77 78 78 77 79 78]\n",
      "  79 (+/- 1.4) <- [79 78 79 81 78 81 77 80 77 78]\n",
      "  78 (+/- 1.1) <- [79 77 78 78 77 76 80 78 77 78]\n",
      "  77 (+/- 1.3) <- [76 78 79 77 77 79 76 76 75 77]\n",
      "  78 (+/- 1.0) <- [76 79 77 79 78 77 77 78 78 76]\n",
      "  78 (+/- 1.7) <- [80 77 78 76 78 76 77 79 77 81]\n",
      "  79 (+/- 1.7) <- [79 80 78 76 80 76 79 75 79 79]\n",
      "  78 (+/- 1.4) <- [78 78 79 77 77 76 76 79 81 78]\n",
      "  78 (+/- 1.5) <- [80 78 77 76 78 78 78 74 79 76]\n",
      "  77 (+/- 1.6) <- [79 76 80 76 75 76 76 75 76 77]\n",
      "  78 (+/- 1.3) <- [76 77 78 78 79 81 78 78 77 77]\n",
      "  79 (+/- 2.2) <- [81 77 80 80 75 75 78 78 82 77]\n",
      "  78 (+/- 1.3) <- [77 79 78 77 78 76 77 80 79 77]\n",
      "  78 (+/- 1.2) <- [77 80 77 78 77 79 78 76 76 79]\n",
      "  78 (+/- 1.1) <- [77 78 77 76 78 78 78 79 76 75]\n",
      "  78 (+/- 1.5) <- [76 80 79 75 76 77 78 77 77 77]\n",
      "Accuracy: 78.0 (+/- 1.59)\n",
      "Mean time (20 cv): 21.10 seconds\n",
      "Overall time: 426.34 seconds\n",
      "\n",
      " -------------------- Baseline -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)\n"
     ]
    },
    {
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       "rwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc213537690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  69 (+/- 2.2) <- [70 68 71 67 64 67 70 68 72 66]\n",
      "  69 (+/- 1.4) <- [71 68 67 67 72 69 67 70 69 70]\n",
      "  70 (+/- 2.0) <- [71 69 73 68 68 71 71 68 71 66]\n",
      "  69 (+/- 1.4) <- [70 67 66 67 67 70 68 70 70 68]\n",
      "  70 (+/- 2.4) <- [71 69 72 68 70 75 71 68 68 66]\n",
      "  71 (+/- 0.7) <- [71 71 70 70 71 71 70 69 70 70]\n",
      "  70 (+/- 1.4) <- [70 68 72 69 69 67 71 69 70 69]\n",
      "  70 (+/- 1.5) <- [69 70 68 71 65 70 70 69 70 69]\n",
      "  69 (+/- 1.7) <- [68 69 68 69 68 65 70 70 69 72]\n",
      "  69 (+/- 1.8) <- [72 69 66 68 69 66 69 69 70 71]\n",
      "  70 (+/- 1.4) <- [71 70 69 69 69 69 73 70 71 68]\n",
      "  70 (+/- 1.6) <- [71 70 71 68 69 69 73 68 69 71]\n",
      "  69 (+/- 1.3) <- [69 70 66 68 69 70 68 71 68 68]\n",
      "  69 (+/- 2.0) <- [71 66 68 66 67 69 66 69 69 72]\n",
      "  70 (+/- 1.1) <- [69 70 72 68 69 70 69 71 71 69]\n",
      "  70 (+/- 2.0) <- [72 71 69 71 67 68 71 72 69 67]\n",
      "  70 (+/- 1.6) <- [67 71 69 69 68 70 71 71 72 69]\n",
      "  70 (+/- 0.9) <- [70 68 71 70 68 70 69 69 71 69]\n",
      "  70 (+/- 1.2) <- [72 71 69 69 71 69 72 70 68 69]\n",
      "  69 (+/- 1.7) <- [67 70 71 67 67 72 67 70 70 69]\n",
      "Accuracy: 69.7 (+/- 1.71)\n",
      "Mean time (20 cv): 16.89 seconds\n",
      "Overall time: 341.74 seconds\n"
     ]
    }
   ],
   "source": [
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    separator('Graph')\n",
    "    %run audio_graph.ipynb\n",
    "if not regen_features:\n",
    "    separator('Features')\n",
    "    %run audio_features.ipynb\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\n",
    "        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])\n",
    "        %run audio_features.ipynb\n",
    "    separator('Classification', True)\n",
    "    %run audio_classification.ipynb\n",
    "    \n",
    "    # Collect results.\n",
    "    for key in res:\n",
    "        res[key].append(globals()[key])\n",
    "\n",
    "# Baseline, i.e. classification with spectrograms.\n",
    "p['dataset_classification'] = 'X'\n",
    "p['scale'] = 'minmax'  # Todo: should be done in pre-processing.\n",
    "if regen_baseline:\n",
    "    res['baseline'] = []\n",
    "    res['baseline_std'] = []\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "        res['baseline'].append(accuracy)\n",
    "        res['baseline_std'].append(accuracy_std)\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "    res['baseline'] = len(Pvalues) * [accuracy]\n",
    "    res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lg = [None, 100]\n",
      "res['accuracy_std'] = [1.6165798945785377, 1.5851743682566213]\n",
      "res['objective_j'] = [0, 8627.8694152832031]\n",
      "res['objective_i'] = [68870.421875, 56812.48046875]\n",
      "res['objective_h'] = [0, 0]\n",
      "res['objective_g'] = [42317.16796875, 71240.95703125]\n",
      "res['baseline'] = [69.729166666666742, 69.729166666666742]\n",
      "res['time_features'] = [1148.192850112915, 2780.502058982849]\n",
      "res['baseline_std'] = 1.71029866625\n",
      "res['sparsity'] = [4.745763422818792, 19.595181417785234]\n",
      "res['iterations_inner'] = [898, 1329]\n",
      "res['iterations_outer'] = [14, 7]\n",
      "res['accuracy'] = [75.918333333333337, 78.01666666666668]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc213c4f950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/numpy/ma/core.py:3847: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
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       "iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\n",
       "iIiIiIiIiIiIiIiIiIiIiIiIiIiIX/8fTaZ4CyYay/MAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc213a7e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc21387f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2135a2410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc24c0f5890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc213bb0610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AJbmDiCpEaavBj8fOBL4d1K7w28Bd+UKJUmSOtut+rk3qe/wPhmzSMKRtaQd/SOwB7ALcApwW944\n",
       "kiRJkiRJkiRJkiRJkiRJkiRJkiRJQ/c/w/Ygi9KampUAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc213bd76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for lg = None\n",
      "Dictionary atoms for lg = 100\n",
      "Experiment time: 5570 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/figure.py:371: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    }
   ],
   "source": [
    "print('{} = {}'.format(Pname, Pvalues))\n",
    "for key, value in res.items():\n",
    "    if key is not 'atoms_D':\n",
    "        print('res[\\'{}\\'] = {}'.format(key, value))\n",
    "\n",
    "def plot(*args, **kwargs):\n",
    "    plt.figure(figsize=(8,5))\n",
    "    x = range(len(Pvalues))\n",
    "    log = 'log' in kwargs and kwargs['log'] is True\n",
    "    pltfunc = plt.semilogy if log else plt.plot\n",
    "    params = {}\n",
    "    params['linestyle'] = '-'\n",
    "    params['marker'] = '.'\n",
    "    params['markersize'] = 10\n",
    "    for i, var in enumerate(args):\n",
    "        if 'err' in kwargs:\n",
    "            pltfunc = plt.errorbar\n",
    "            params['yerr'] = res[kwargs['err'][i]]\n",
    "            params['capsize'] = 5\n",
    "        pltfunc(x, res[var], label=var, **params)\n",
    "        for i,j in zip(x, res[var]):\n",
    "            plt.annotate('{:.2f}'.format(j), xy=(i,j), xytext=(5,5), textcoords='offset points')\n",
    "    margin = 0.25\n",
    "    params['markersize'] = 10\n",
    "    plt.xlim(-margin, len(Pvalues)-1+margin)\n",
    "    if 'ylim' in kwargs:\n",
    "        plt.ylim(kwargs['ylim'])\n",
    "    plt.title('{} vs {}'.format(', '.join(args), Pname))\n",
    "    plt.xlabel(Pname)\n",
    "    plt.ylabel(' ,'.join(args))\n",
    "    plt.xticks(x, Pvalues)\n",
    "    plt.grid(True); plt.legend(loc='best'); plt.show()\n",
    "\n",
    "def div(l):\n",
    "    div = Pvalues if Pname is l else [p[l]]\n",
    "    return np.array([1 if v is None else v for v in div])\n",
    "\n",
    "# Classification results.\n",
    "res['chance'] = len(Pvalues) * [100./p['Ngenres']]\n",
    "res['chance_std'] = 0\n",
    "err=['accuracy_std', 'baseline_std', 'chance_std']\n",
    "plot('accuracy', 'baseline', 'chance', err=err, ylim=[0,100])\n",
    "\n",
    "# Features extraction results.\n",
    "if regen_features:\n",
    "    plot('objective_g', 'objective_h', 'objective_i', 'objective_j', log=True)\n",
    "    # Unweighted objectives.\n",
    "    print('g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)')\n",
    "    res['objective_g_un'] = res['objective_g'] / div('ld')\n",
    "    res['objective_h_un'] = res['objective_h'] / div('le')\n",
    "    res['objective_i_un'] = res['objective_i'] / div('ls')\n",
    "    res['objective_j_un'] = res['objective_j'] / div('lg')\n",
    "    plot('objective_g_un', 'objective_h_un', 'objective_i_un', 'objective_j_un', log=True)\n",
    "    plot('sparsity', ylim=[0,100])\n",
    "    plot('time_features')\n",
    "    plot('iterations_inner')\n",
    "    plot('iterations_outer')\n",
    "\n",
    "    for i, fig in enumerate(res['atoms_D']):\n",
    "        print('Dictionary atoms for {} = {}'.format(Pname, Pvalues[i]))\n",
    "        fig.show()\n",
    "\n",
    "print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))"
   ]
  }
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